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Published on: August 29, 2018
PPG delineator for real-time ubiquitous applications.
Umar Farooq1, Dae-Geun Jang, Jang-Ho Park
1U-Health Lab, Department of Biomedical Engineering, Kyung Hee University, South Korea.
Summary
This study introduces a real-time signal processing algorithm for accurate detection of onsets and peaks in Photoplethysmogram (PPG) signals, crucial for wearable health devices.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Wearable Technology
Background:
- Photoplethysmogram (PPG) signals are widely used for non-invasive physiological monitoring.
- Accurate detection of waveform features like onsets and peaks is essential for reliable analysis.
- Existing algorithms may struggle with varying signal quality and computational demands for ubiquitous devices.
Purpose of the Study:
- To develop and validate a real-time signal processing algorithm for robust detection of PPG onsets and peaks.
- To ensure the algorithm's applicability in compact, low-power health monitoring systems.
- To improve the accuracy and reliability of PPG feature extraction.
Main Methods:
- A four-stage algorithm involving signal transformation, pulse detection using amplitude and inter-beat intervals, and adaptive peak/onset searching.
- Implementation of low computational complexity signal processing steps and decision logic.
- Adaptive parameter adjustment to accommodate beat morphology variations and baseline fluctuations.
Main Results:
- The algorithm successfully detects onsets and peaks in PPG waveforms.
- Achieved high performance metrics: 96.89% sensitivity and 94.55% positive predictivity.
- Operates within a 12 ms acceptance level, demonstrating real-time capability.
Conclusions:
- The developed algorithm provides accurate and reliable real-time detection of PPG onsets and peaks.
- Its low computational complexity makes it suitable for integration into ubiquitous health monitoring devices.
- The adaptive nature enhances robustness against signal variations.